Wind Power Utilization Research Articles

Offshore Wind Power: Progress of the Edge Tool, Which Can Promote Sustainable Energy Development

Offshore wind is renewable, clean, and widely distributed. Therefore, the utilization of offshore wind power can potentially satisfy the increasing energy demand and circumvent the dependence on fossil energy. Thus, offshore wind power is an edge tool for achieving sustainable energy development because of its potential in large-scale energy supply and its important role in reducing environmental pollution as well as carbon emission brought by fossil energy. The worldwide development of offshore wind power has entered the era of large-scale research and commercial application. It displays a trend of rapid development, continuous technological breakthroughs, and high-speed market growth. This article systematically introduces the structural components and technical characteristics of offshore wind power. Moreover, the current developmental status of offshore wind power is summarized. By reviewing the current development and application status of offshore wind power technology worldwide, large wind turbines and fixed and floating offshore wind power technologies are analyzed. Additionally, the development of the offshore wind energy market is overviewed. The policy condition and key aspects such as the construction, operation, and maintenance of offshore wind power plants are also summarized. Finally, the prospective challenges and development trends of offshore wind power and its significance in achieving sustainable society development are proposed. We consider that the article can provide reference and inspiration for researchers and developers dedicated to offshore wind power.

Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making.

System Optimization and Operating Strategy of Single-Stage Air Source Heat Pump with Thermal Storage to Reduce Wind Power Curtailment

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Operational characteristics of an integrated island energy system based on multi-energy complementarity

This study addresses the intermittent renewable energy supply and the large footprint of battery storage on an island reef in China by proposing an integrated energy system that incorporates hydrogen production, storage, and utilisation. Mathematical models for wind and photovoltaic power generation, energy storage, hydrogen production and utilisation, diesel generators, and energy management systems are established. Additionally, an integrated energy system is constructed using Simulink software to simulate and analyse its operational characteristics in various seasonal scenarios. The simulation results indicate that the surplus from wind power and photovoltaic systems, after supplying the load for over 70 % of the day, constitutes more than 40 % of the load demand. The energy storage system can maintain a maximum charging rate for more than 50 % of the day and a maximum discharge rate for less than 16.7 % of the day. The electrolysis tank produces hydrogen for more than 71 % of the day and simultaneously consumes up to 750 % of the load. The fuel cell consumes hydrogen for less than 12.5 % of the day and simultaneously provides up to 27 % of the load. Analysis revealed that the hydrogen system improves the energy utilisation, the control strategy realises stable supply and demand within the system.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Multistep short‐term wind speed prediction with rank pooling and fast Fourier transformation

AbstractShort‐term wind speed prediction is essential for economical wind power utilization. The real‐world wind speed data are typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multistep wind speed prediction, namely, LR‐FFT‐RP‐MLP/LSTM (linear fast Fourier transform rank pooling multiple‐layer perceptron/long short‐term memory). Our hybrid model processes the local and global input features simultaneously. We leverage RP for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit FFT to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are, respectively, integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared with state‐of‐the‐art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.

Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response

An optimization scheduling method for cogeneration systems based on the Q-learning-based memetic algorithm (QMA) is developed to enhance the system's wind power absorption capacity and economical operation. First, an optimal scheduling model for cogeneration systems is constructed. The model integrates time-of-use electricity pricing and heating comfort into the demand side, fully exploiting the potential of demand-side response (DR) in power generation and heating. An additional heat source (AHS), which consists of an electric boiler and heat storage tank, is used to uncouple heat and power. In addition, a QMA algorithm is designed to find the optimal operating scheme for the cogeneration system. The Q-learning algorithm is introduced to dynamically adjust crossover and mutation parameters during the global evolution stage, improving the algorithm's search capability. The Taguchi method is utilized for algorithm parameter calibration. Finally, the simulation results under various operating scenarios are compared and analyzed, verifying the feasibility and effectiveness of the proposed method. The simulation results show that DR and AHS can improve the system's economic performance and wind power utilization rate. Compared with the IMA, IPSO, MA, and IABC algorithms, the QMA algorithm reduces the average economic cost by approximately 1.18 %, 2.23 %, 5.69 %, and 14.69 %, respectively.

A combined model based on POA-VMD secondary decomposition and LSTM for ultra-short-term wind power forecasting

As the application of wind power expands, precise prediction of wind energy becomes essential for the effective plan and reliable functioning in the realm of the power system. Aiming to enhance wind power utilization efficiency and minimize error relating to ultra-short-term wind power forecasting, a novel model grounded in sliding time window, Pelican optimization algorithm-variational mode decomposition (POA-VMD) secondary decomposition, sample entropy calculation, sequence reconstruction, and long short-term memory (LSTM) prediction is introduced in this paper. First, in the training set, the sliding time window technique is employed to identify the optimal parameters for the forecasting algorithm, aiming to closely replicate the actual forecasting performance. Subsequently, the VMD algorithm is enhanced through optimization with the POA. This involves utilizing POA to dynamically ascertain the optimal parameters [k, α] for VMD, allowing for an adaptive decomposition of the raw wind power data sequence and effectively diminishing data noise. After calculating each modal's sample entropy, the modal with the highest sample entropy is further decomposed using POA-VMD. The decomposed sequence is predicted using LSTM to get the final prediction. The experiment ultimately demonstrated that the introduced model markedly improves the accuracy of forecasting. By adding POA-VMD secondary decomposition residuals, the prediction errors, as measured by mean absolute error, root mean square error, and mean absolute percentage error, are decreased by 52.03%, 30.34%, and 39.87%, respectively, and coefficient of determination (R2) is increased by 7.75%.

Stochastic optimization scheduling of wind-photovoltaic-storage-hydrogen-water system in independent island zero-carbon microgrid

A stochastic optimal scheduling strategy is being proposed for a wind-photovoltaic-storage-hydrogen-water system within a zero-carbon microgrid for independent islands. This strategy is aimed at addressing the challenge of sustainable energy resource supply prevalent in traditional islands, while also considering the demand response of seawater desalination. In order to harness the clean, low-carbon, and high-efficiency characteristics of hydrogen, an electrolytic water hydrogen production system is integrated into the power system to utilize wind power generation. The desalination equipment is regarded as a controllable load within the system. It is combined with a demand response strategy to maximize the full utilization of the desalination equipment for consuming wind power on the island. Furthermore, a two-stage stochastic optimization method is being employed to handle uncertainties related to wind and photovoltaic generation. The objective is to minimize the system’s operating cost. To solve this optimization problem, the commercial solver CPLEX is being utilized. Through comparative analysis across different scenarios, simulation results indicate that the proposed strategy effectively dissipates uncertainties in wind scenarios while concurrently reducing the overall system operation cost. This approach comprehensively considers the system’s economics, thus demonstrating its potential to efficiently promote wind power consumption.

Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model

Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed.

Two-stage planning model of power-to-gas station considering carbon emission flow

The widespread use of fossil fuels has substantially contributed to global climate change. One of the most crucial ways to address climate change is to minimize carbon emissions in the energy sector. Power-to-Gas (P2G) technology provides a potential solution. In this context, this paper introduces a novel two-stage P2G planning method for the electric-gas coupling system, using carbon emission flow technology. The carbon emission flow model employed facilitates the distribution of carbon emissions across the entire energy network. Central to our planning strategy is the identification of nodes with reduced carbon emissions for the siting of P2G facilities, predicated on the premise that such nodes are indicative of significant renewable energy utilization. Constructing P2G systems at these particular nodes is strategically advantageous for augmenting renewable energy consumption. In the initial phase, a P2G siting planning methodology predicated on a low-carbon criterion is advanced, resulting in a definitive siting scheme. Subsequently, the second phase involves establishing a P2G capacity optimization model that seeks to strike an economic equilibrium between the utilization of surplus wind power and P2G operational investment. An enhanced pigeon optimization algorithm is employed to resolve the proposed model. Our method is verified by simulation. Compared with other conventional models, the accommodation capacity of renewable energy with the proposed method is enhanced, and the carbon emissions and operating costs of IES are also reduced.

Multi‐factor Analysis of Capacity Allocation Optimization for Novel Wind‐Hydrogen Coupled Power Generation Systems

Wind power generation has the problem of wind resource waste. Wind‐hydrogen coupled can enhance wind power's utilization and revenue. Currently, there is no discussion of the impact of multiple factors on the system configuration. In this study, take the annual profit of the wind‐hydrogen coupled power generation systems (WHCPGS) as the objective function, and construct the multi‐factor capacity configuration model. Using particle swarm optimization (PSO) to solve the model, and analyzes the impact of multi‐factors on the system's annual profit and revenue mode. The results show that with 30 wind turbines, the coupled system annual profit increases by 43% compared to the alone system. The higher investment does not make the system more rewarding. The configuration results are the same for investments of 0.8 billion ¥ and 1 billion ¥. The price at 0.20 ¥ kW−1 h−1, the coupled system has the optimal annual profit. Fuel cells’ (FCs) electricity price and hydrogen transportation distance are crucial factors influencing the mode of the system profit. This study has specific reference significance for the practical application of capacity configuration of the system.

Facilitating interaction between partial differential equation-based dynamics and unknown dynamics for regional wind speed prediction

Regional wind speed prediction is an important spatiotemporal prediction problem which is crucial for optimizing wind power utilization. Nevertheless, the complex dynamics of wind speed pose a formidable challenge to prediction tasks. The evolving dynamics of wind could be governed by underlying physical principles that can be described by partial differential equations (PDE). This study proposes a novel approach called PDE-assisted network (PaNet) for regional wind speed prediction. In PaNet, a new architecture is devised, incorporating both PDE-based dynamics (PDE dynamics) and unknown dynamics. Specifically, this architecture establishes interactions between the two dynamics, regulated by an inter-dynamics communication unit that controls interactions through attention gates. Additionally, recognizing the significance of the initial state for PDE dynamics, an adaptive frequency-gated unit is introduced to generate a suitable initial state for the PDE dynamics by selecting essential frequency components. To evaluate the predictive performance of PaNet, this study conducts comprehensive experiments on two real-world wind speed datasets. The experimental results indicated that the proposed method is superior to other baseline methods.

Energy optimal scheduling strategy considering V2G characteristics of electric vehicle

in recent years, large-scale electric vehicles connected to the power grid has a significant impact on demand-side resources. In addition, the vehicle-to-grid technology of electric vehicles introduces more uncertainties, which brings great challenges to the optimal scheduling of integrated energy systems. To solve this problem, this paper fully considers the uncertainty and fluctuation of wind power. Firstly, the objective function of joint configuration is constructed to maximize the utilization of wind power and minimize the planned generation cost of the system. Secondly, the linearized power flow equations are used to represent the relations among the state variables, and the second-order cone relaxation method is applied to deal with the branch power flow constraints, which is transformed into a solvable mixed-integer second-order cone programming model. Finally, the validity of the model is verified by an example. The system achieves obvious peak-shaving and valley-filling effect and improves the economic benefit of the system.

Wind power forecasting based on a machine learning model: considering a coastal wind farm in Zhejiang as an example

ABSTRACT The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model’s predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model’s accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China.

Distributed energy management of multi-area integrated energy system based on multi-agent deep reinforcement learning

This paper addresses the problem of distributed energy management in multi-area integrated energy systems (MA-IES) using a multi-agent deep reinforcement learning approach. The MA-IES consists of interconnected electric and thermal networks, incorporating renewable energy sources and heat conversion systems. The objective is to optimize the operation of the system while minimizing operational costs and maximizing renewable energy utilization. We propose a distributed energy management strategy that makes hierarchical decisions on intra-area heat energy and inter-area electric energy. The strategy is based on a multi-agent deep reinforcement learning framework, where each agent represents a component or unit in the MA-IES. We formulate the problem as a Markov Decision Process and employ Q-learning with experience replay and double networks to train the agents. The proposed strategy is evaluated using a simulation of a four-area MA-IES. The results demonstrate significant improvements in energy management compared to traditional methods, with higher renewable energy utilization and lower operational costs. Specifically, the strategy achieves 100% utilization of wind power, and decreases operational costs by 5.53%. Furthermore, it leverages the generalization capabilities of reinforcement learning to respond in real-time to uncertainties in demand and wind power output. The results highlight the advantages of the proposed strategy, making it a promising solution for optimizing the operation of multi-area integrated energy systems.

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

A study on green hydrogen production potential of Canada with onshore and offshore wind power

This study aims to develop a unique approach to examine the possibility of hydrogen generation through the utilization of renewable energy sources, specifically onshore and offshore wind power in Canada. The wind energy exhibits great potential for hydrogen production inseveral provinces of the country. Proton Exchange Membrane (PEM) type electrolysers are considered as viable technologies for the process of water splitting and subsequent hydrogen production. According to the calculations, the overall green hydrogen production potential of Canada is found to be 439.93 Mt. The potential for green hydrogen production from onshore wind energy is calculated to be 402.63 Mt, whereas the potential from offshore wind energy is estimated to be 37.29 Mt. In relation to this matter, it is notable that Québec, British Columbia and Ontario exhibit the highest proportions among the provinces in terms of hydrogen production potential, amounting to 80.31 Mt, 69.47 Mt, and 51.88 Mt, respectively. A specific analysis methodology is utilized to determine the hydrogen production potentials for each province in Canada, with a focus on its potential application in a hydrogen-based economy. Advancing a sustainable future by creating local, national, and global connections and networks. Furthermore, it offers a thorough analysis of the potential influence of wind energy in improving the overall sustainability of the country. The results of this study are anticipated to establish a strong basis for the creation of plans and strategies essential for the country, specifically in the formulation of new energy policies focused on utilizing resources for renewable energy.

A multi-scenario evaluation of the energy transition mechanism in the Philippines towards decarbonization

The Philippines relies heavily on fossil fuels for energy, posing environmental issues. To counter this, the study suggests increasing the use of renewable energy sources like wind and solar power. By optimizing the electricity supply mix, the study shows that coal usage can be reduced by 37.23%, with solar power accounting for 20.07% and wind power accounting for 8.83% without using Battery Energy Storage System (BESS). By incorporating Battery Energy Storage Systems (BESS) and scaling up the utilization of solar and wind power, which is aligned with the country's 2030 target, coal consumption can be reduced by 30.44%. However, transmission expansion may have limited effects due to the country's archipelagic nature. Therefore, a decentralized approach is necessary to minimize coal usage and encourage renewable energy sources. The study uses linear programming optimization methods to explore scenarios that can mitigate climate change by increasing solar and wind power use, reducing coal usage, and investigating the benefits of transmission line expansion and energy storage integration for enhancing grid flexibility.

Numerical Investigation on Aerodynamic Performance of Helical Savonius Rotor Inspired by Natural Shapes

There have been extensive studies conducted on Horizontal Axis Wind Turbines (HAWT) at relatively moderate to high wind speed regions. However, such detailed investigations for Vertical Axis Wind Turbines (VAWT), specifically for low wind speed terrains are amply reported. This motivates us to conduct research to explore possibilities in improving performance of VAWT in low wind speed terrains, which is attempted in the present work. This is required due to the fact that most of regions do not have sufficient extractable wind energy due to low speeds. VAWTs can perform at such low wind speed, but are less efficient. Improving efficiency of VAWT will solve the purpose. Hence, the present study is aimed at finding the performance characteristics of VAWT for low Wind speed configurations. Various parameters affecting power generation are investigated. Numerical analyses on various configurations are conducted to study the effects of twist angle, free stream velocity, number of blades. Computational results obtained have been in good agreement with the established results for semi-circular Savonius rotor profile. The results suggest that for low wind speed terrains, there is a need to explore the combination of lift and drag type of profiles, which could be used for the utilization of available wind power. Hence, naturally inspired shapes (profiles) were investigated for the possible solution of combined lift and drag type wind turbines at low speeds. The blade shape for such combined lift and drag type wind turbine were deduced from the available literature. It is well established that the naturally inspired shapes as noted in sea conch follow golden ratio in its contours. The present study provides an insight on the characteristic curves of VAWT for low wind speed terrains, effects of various geometric and flow parameters suitable for low wind speed terrains.